1
Global warming and energy yield evaluation of Spanish wheat straw electricity generation
2
– A LCA that takes into account parameter uncertainty and variability.
3 4
Sastre C. M.1*, González-Arechavala Y.1 and Santos A. M.1.
5 6
(1) Institute for Research in Technology - ICAI School of Engineering - Comillas Pontifical
7
University. Alberto Aguilera 23, 28015 Madrid (Spain) Phone: +34 915422800, Fax: +34
8
915423176
9
*Corresponding author:
[email protected]
10 11
Abstract
12
This paper aims to provide more accurate results in the life cycle assessment (LCA) of
13
electricity generation from wheat straw grown in Spain through the inclusion of parameter
14
uncertainty and variability in the inventories. We fitted statistical distributions for the all the
15
parameter that were relevant for the assessment to take into account their inherent uncertainty
16
and variability. When we found enough data, goodness of fit tests were performed to choose the
17
best distribution for each parameter and, when this was not possible, we adjusted triangular or
18
uniform distributions according to data available and expert judge. To obtain a more complete
19
and realistic LCA, we considered the consequences of straw exportation for the agricultural
20
system, specially the loss of soil organic carbon and the decrease of future fertility. We also
21
took into account all the inputs, transformations and transports needed to generate electricity in
22
a 25 MWe power plant by straw burning. The inventory data for the agricultural, the transport
23
and the transformation phases were collected considering their most common values and ranges
24
of variability for the Spanish case. We used Monte Carlo simulation and sensitivity analysis to
25
obtain global warming potential (GWP) and fossil energy (FOSE) consumption of the system.
26
These results were compared with those of the electricity generated from natural gas in Spanish
27
power plants, as fossil reference energy system. Our results showed that for the majority of the
28
simulations electricity from wheat straw biomass combustion produced less greenhouse gases
29
(GHG) emissions and consumed less fossil energy than electricity from natural gas. However,
30
only 58 % of the simulations achieved the sustainability threshold of 60 % GHG savings
31
proposed by the European Union (EU). Our analysis showed that agricultural field works and
32
the loss of soil organic carbon due to straw exportation were the most important phases for
33
FOSE consumption and GWP respectively. According to parameters sensitivity analysis, the
34
loss of soil organic carbon was completely dependent on the isohumic coefficient and the soil
35
carbon content factor values. Due to this fact, local and specific estimates of these parameters
36
are relevant tasks to be performed in order to reduce uncertainties and provide a definitive
37
answer to the compliance of the EU sustainability criteria.
38 39
Keywords: bioenergy; crop residues; life cycle assessment (LCA); global warming potential;
40
uncertainty; sustainability criteria
41
42
1. Introduction
43
With the purpose of reducing greenhouse gases (GHG) emissions and depletion of non
44
renewable energy sources, the European Union (EU) Member States have committed
45
themselves to increase the share of renewable energy in the EU's energy mix to 20% and reduce
46
GHG emissions by 20% by 2020 [1]. Bioenergy is intended to play a central role in the
47
accomplishment of these objectives accounting for more than half of projected 2020 Europe’s
48
energy output [2]. Potential GHG reductions of different pathways to produce bioenergy from
49
solid [3,4] and liquid [5-8] are being evaluated intensively [9]. Several studies suggest that
50
lignocellulosic biomass sources for heat and electricity production may perform better in GHG
51
assessments when compared with crops used as feedstock for first generation liquid biofuels
52
[10-13]. Crop residues [14-17] and dedicated crops [3,4] can be used as source of
53
lignocellulosic biomass for bioenergy whereas the first ones usually offer higher resource
54
efficiency [2]. Furthermore, the use of dedicated energy plantations for bioenergy in Europe
55
could produce indirect land use changes rising food prices in other countries. For these reasons,
56
the use of crop residues as lignocellulosic feedstock for heat and electricity production can be
57
considered as a techno-economic feasible option [18-24] that helps to accomplish previously
58
mentioned objectives [16,25].
59
Wheat is one of the most important Spanish rainfed crops in terms of its cropping surface and
60
total production per year with 2.0 million ha and 5.3 dry Mt in average over the last decade [26],
61
thereby approximately 5.3 dry Mt of wheat straw corresponding to 90 PJ of biomass energy are
62
jointly produced every year. In most of the Spanish regions, there is an excess of straw due to
63
the small relevance of ruminant livestock and/or the preference for barley as source of energy
64
for its feeding. In this situation, the surplus wheat straw is usually incorporated into the soil
65
aiming to maintain or to increase soil organic matter stock. In the described framework, wheat
66
straw could be alternatively sold to biomass power plants as feedstock for electricity generation,
67
instead of being incorporated into the soil. This alternative use of straw can help to reduce the
68
current national energy dependence and to accomplish EU objectives of GHG reduction and
69
renewable energies share. Nevertheless, a Life Cycle Assessment (LCA) of this use should be
70
performed to evaluate environmental impacts and in particular GHG emissions reduction, as the
71
European Commission recommends for solid biomass production for electricity generation,
72
heating and cooling [27]. Special attention should be paid to the effect of straw exportation in
73
GHG emissions due to the possible future decrease of crop fertility and soil carbon stocks
74
[14,28].When straw is left into the field a fraction of its carbon content ends up forming humus
75
(effective organic matter) and consequently a carbon loss should be accounted when straw is
76
removed. Besides, compensation should be given to the loss of fertility due to the positive effect
77
of this humus in crop fertility and the nutrients that straw supplies for future crops. There is a
78
huge uncertainty regarding these facts among others like wheat straw yields, straw humidity and
79
energy content, N2O emissions [29,30], diesel consumption of field works, etc. Uncertainty and
80
variability has been discussed as an important aspect to deal with in the field of LCA in general
81
[31-33] as well as for the particular case of bioenegy LCAs [7,8,34]. In this study we made a
82
systematic consideration of parameter uncertainty and variability of all the parameters affecting
83
all the previous aspects, retrieving data from all the references we found that were valid for our
84
case. We adjusted statistical distributions for all parameters that have a significant effect in the
85
results performing goodness of fit tests to choose the best option among distributions when
86
enough data were available. When data were scarce for a parameter we adjusted triangular or
87
uniform distributions taking into account the data available and our knowledge of the
88
phenomenon being studied. There are several LCAs that evaluate use of wheat straw for
89
combined heat and power (CHP) [14-17] but we are not aware of any published LCA including
90
parameter uncertainty and variability systematically for all the aspects described previously.
91
The objective of this study is to perform an accurate evaluation of the environmental
92
sustainability of the use of wheat straw for electricity generation in Spain. To this end, we
93
conducted an uncertainty LCA of energy balances and GHG emissions of electricity obtained
94
from wheat straw and compared the results with those of electricity obtained from natural gas in
95
Spanish power plants. Monte Carlo simulation and sensitivity analysis were implemented to
96
incorporate uncertainty and variability affecting to the LCAs produced. GHG savings of
97
Spanish straw electricity were calculated and compared with thresholds suggested by the EU as
98
sustainability criteria [27]. Shares of the phases considered in the assessment and critical
99
parameters affecting results were discussed suggesting possible improvements.
100 101
2 Energy and environmental assessment methodology
102 103
2.1. Goal, scope and evaluation of data sources and tools
104
We selected LCA as the environmental assessment tool to determine energetic and
105
environmental performance (GHG emissions) of the use of wheat straw to generate electrical
106
energy and its comparison with a fossil reference energy system.
107
LCA involves a systematic set of procedures for compiling and examining the inputs and
108
outputs of materials and energy and the associated environmental impacts directly attributable to
109
the functioning of a product or service system throughout its life cycle [35]. This environmental
110
management tool is regulated by ISO 14040 [35] and ISO 14044 [36] standards, and according
111
to them, LCAs should follow four steps: (1) goal and definition, (2) inventory analysis, (3)
112
impact assessment and (4) interpretation.
113
The most important data sources used for the modelling of the inventories of bioenergy system
114
were: FAOSTAT [26] for wheat yields, ECN Phyllis 2 database [37] for straw energy content,
115
BIOBID database [38] for straw nutrient contents, the Spanish platform of agricultural
116
machinery for machinery amortization and for diesel and motor oil consumptions of field works
117
[39], the IPPC [40] and The RSB Methodology [41] for N2O emissions and real data of a
118
Spanish 25 MWe straw power plant for the generation of electricity.
119
We used previous data sources to include parameter uncertainty and variability in order to
120
provide more accurate results which try to be representative of the different Spanish agricultural
121
realities. The generation of electricity from natural gas under Spanish conditions has been
122
chosen as the reference system for comparisons to be one of the cleanest fossil energy sources
123
for electricity generation [42].
124
Simapro 7.3 software tool and Ecoinvent 2.2 [43] European database have been selected to
125
conduct the LCAs in this study. For both, the bioenergy system and the natural system
126
parameter uncertainty and variability were incorporated in the inventories.
127 128
2.2. Functional unit
129
The generation of 1 TJ of electrical energy from Spanish straw burned in a 25 MWe power plant
130
have been selected as functional unit for the biomass energy system. The generation of 1 TJe
131
from natural gas in Spanish conditions has been chosen for the reference fossil system (see Fig.
132
1).
133 134
2.3 Systems description
135
The characteristics and burdens of the bioenergy system and the natural gas system are
136
described in this section. We chose natural gas Spanish electricity as reference system for
137
comparisons because is the cleanest fossil energy source available.
138 139
2.3.1 Bioenergy system
140
The bioenergy system is composed of three subsystems: wheat straw production (agricultural
141
system), biomass power plant and transport that are going to be explained in detail. The Fig 1.
142
summarises the processes included in the bioenergy system.
143
144 145 146
Fig. 1 Bioenergy system burdens and phases included in the analysis.
147
(1) Wheat straw production subsystem.
148
The objective of wheat cultivation is obtaining grain usually for alimentary purposes. Due to
149
this fact, wheat straw is considered as a residue and all the inputs and field works needed to
150
grow wheat in order to produce grain are not considered. Only differences between the wheat
151
cultivation reference system (straw incorporated in the soil) and the studied system (straw
152
exported to generate electricity) should be accounted in the LCA of electricity production from
153
wheat straw.
154
There are some differences in the field works due to straw exportation that should be borne in
155
mind. During the harvesting the straw is not chopped because is going to be baled and
156
operations of baling and bale loading are needed. The amount of machinery amortized in these
157
field works as well as the diesel and motor oil consumption and combustion emissions have
158
been accounted (see later Sections 2.4.2 and 2.4.3).
159
Other difference is the reduction of N2O emissions due to the lower amount of straw left in the
160
field (see later Section 2.4.4). Straw nitrogen content is crucial for the estimation of these
161
emissions.
162
One more difference is the reduction of crop fertility in future years due to the effect of the loss
163
of humus and N, P and K nutrients that straw supplies in the wheat cultivation reference system.
164
This loss of fertility of the soil has been compensated in our modelling through crop
165
intensification by the use of more fertilizers. Cherubini states that straw fertilizer effect ranges
166
from 25% to 75% of its N, P and K straw contents [14], so we consider uniform distributions
167
following this figures and log-normal distributions for N, P and K straw contents (see later
168
section 2.4.5). The intensification by the use of nitrogen fertilizer produces a raise in N2O
169
emissions that have been also accounted (see later Section 2.4.4).
170
The last and most remarkable difference is the loss of soil organic carbon (SOC) due to straw
171
exportation. We consider in our modelling the loss of SOC due to one year of straw exportation
172
as follows:
173
CO2SOC (kg CO2/ha) = 44/12∙GY SG IHC∙CSOM
174
where CO2SOC (kg CO2/ha) are the CO2 emissions due to land use changes in soil organic
175
carbon; 44/12 is the factor to convert C in CO2; GY(kg grain/ha) is the wheat grain yield in dry
176
basis; SG(kg straw/Kg grain) is the straw / grain ratio for Spanish conditions; IHC(kg OM/kg
177
Straw) is the isohumic coefficient and measures the amount of straw that turns into humus
178
(effective Organic Matter); CSOM(Kg C/kg OM) is the carbon content of soil organic matter.
179
The values that previous parameters take for this study including the uncertainty and variability
180
considered for them can be shown in the section 2.4.5.
181 182
(2) Biomass power plant subsystem.
183
The biomass power plant is modelled using real data from a 25 MWe Spanish straw power plant
184
supplied by power plant officers. This plant is considered representative of other existing
185
biomass power plants in Spain.
186
The straw power plant consumes small amounts of natural gas in start-up and pre-heating and
187
generates ashes and slags as residues of straw burning. Natural gas average consumption and
188
ash and slag average generation per kg from straw burned are shown in Table 1. Aerial
189
emissions are submitted online to regional authorities and its average values are presented in
190
Table 1 as well. The emissions accounted in the assessments are only those which affect the
191
global warming potential (GWP). Fossil carbon dioxide emissions of natural gas burning are
192
taken into account. The emissions of carbon dioxide from straw combustion have not been
193
accounted because CO 2 was previously fixed from the air by the crop no more than one year
194 195 196 197
before being burned. Table 1 Biomass power plant consumptions, residues and emissions Items Type Amount Units Natural gas Consumption 0.0389 MJ/Kg Dry Biomass Slags Residue 93.72 g/Kg Dry Biomass Ashes Residue 9.38 g/Kg Dry Biomass Carbon Dioxide 2.16 Emission g/Kg Dry Biomass from natural gas Emission g/Kg Dry Biomass Nitrogen oxides 1.85 Emission g/Kg Dry Biomass Carbon monoxide 1.05 Emission g/Kg Dry Biomass Sulphur dioxide 0.36 Emission g/Kg Dry Biomass Particulates 0.27
198 199
All the factors of uncertainty and variability affecting the power generation subsystem
200
parameters are described in section 2.4.5.
201 202
(3) Transport subsystem.
203
The main characteristics of transport system are shown in Table 2, the information shown
204
includes: materials to transport, origin and destination points, distances, and means of transport
205
used.
206
Transport distances and transport means of agricultural inputs until regional storehouses are
207
considered as in Ecoinvent [44]. We assumed 10 km as a good estimate of the average transport
208
distance for agricultural inputs from the storehouse to farmer’s plots. Officers in charge of the
209
power plant operation provided average transport distances and transport means for straw bales,
210
ashes and slags.
211
The biomass transport distance from farmer's plots to biomass power plant is calculated as
212
follows:
213
SBTD(km) = ((CSYN/100)/(2*л*CLA))0.5
214
where SBTD(km) is straw bales transport distance; CSYN(ha) is the crop surface needed to
215
achieve the annual electricity production of the biomass plant working 330 days uninterruptedly
216
per year and CLA(available ha/total ha) is the crop land available in the region to grow wheat
217 218 219
and collect the straw for bioenergy purposes. Table 2 Transport system characteristics Material From
To
Manufacturer
Regional storehouse
Regional storehouse
Plot
Distance 600 km 100 km
Fertilizers
220
Straw bales Ash and slag
Farmer plot Biomass power plant
Biomass plant Disposal site
10 km 56 km 37 km
Vehicle Train Lorry >16t Tractor and Trailer Lorry 1632t Lorry 1632t
221
The output of the whole bioenergy system is the electrical energy generated. This energy output
222
is calculated as follow:
223
E(MJe/ha)=GY∙SG/(1-H)∙(1-SL)∙NHVCP,H∙η
224
where E(MJe/ha) is the electrical energy generated; GY(kg grain/ha) is the wheat grain yield in
225
dry basis; SG(kg straw/kg grain) is the ratio between the straw and the grain production; H(kg
226
water/kg wet straw) is the water content of wheat straw in a per unit basis; SL(kg loss/kg straw)
227
are the losses of straw due to bales storage in the plot in a per unit basis; NHVCP,H(MJ/kg) is the
228
net heating value of wheat straw at constant pressure at water content (H): NHVCP,H(MJ/kg) =
229
NHVCP,0∙(1- H) - 2,444∙H and η(MJe/MJ straw) is the conversion efficiency of wheat straw into
230
electricity in a per unit basis.
231
The values that prior parameters take for this assessment are presented in Table 3.
232 233
234
2.3.2 Natural Gas System
235
The natural gas system models the generation of electricity using natural gas in power plants
236
under Spanish conditions. The system considers the exportation of natural gas to Spain of main
237
exporter countries (Algeria 73% and Norway 27%), including the gas field operations for
238
extraction, the losses, the emissions, and the purification. The long distance travel to Spain as
239
well as the transportation to Spanish power plants is considered, including energy consumption,
240
losses and emissions. Average efficiencies as well as inputs needed and typical emissions of
241
Spanish natural gas power plant are taken into account [42]. LCI of the previous system
242
considers parameter uncertainty according to Ecoinvent modelling.
243 244
2.4. Life cycle inventory structure
245
The inventories used to consider natural gas consumption of the biomass power plant and the
246
transportation of agricultural inputs, biomass and power plant residues are taken from Ecoinvent
247
v2.2. The methods used for the inventory analysis of the agricultural system mainly follow
248
those proposed in the life cycle inventories of agricultural production systems [44]. The
249
calculation of N2O emissions is based on the guidelines of the RSB GHG Calculation
250
Methodology v 2.1 [41].
251 252
2.4.1 Fertilizers production
253
The inventories for fertilizers production include the consumption and transport of raw
254
materials and intermediate products as well as the energy consumption and the emissions
255
generated in the production processes [44].
256 257
2.4.2 Diesel and motor oil consumption and combustion emissions of agricultural
258
machinery
259
The diesel and motor oil consumption of agricultural machinery are obtained from the Spanish
260
platform for the knowledge of agricultural machinery [39]. We considered the difference in field
261
works between incorporating the straw into the soil and exporting it. This implies subtracting
262
the consumption of chopping straw and accounting the consumptions for baling and bale
263
loading. The platform provides for each fieldwork, lifetime of tractor or harvester and
264
implements used, power of the tractor or harvester, operation time and diesel and motor oil
265
consumption per hectare. Changing the conditions in operations performed from best to worst
266
(more consuming) possible ones is how we establish the range for diesel and oil consumption
267
factor (DOCF) shown in Table 3. Diesel and oil consumption depend on biomass productivity
268
and this is taken into account apart from the DOCF. According to the Spanish platform [39] the
269
consumption of motor oil for tractors is 1 % of the diesel consumption.
270
The inventories for the extraction, the transportation of crude oil, its transformation into diesel,
271
and its distribution are taken from Ecoinvent [45]. Exhaust emissions from agricultural
272
machinery engines are also taken into account [46].
273 274
2.4.3 Agricultural machinery manufacture
275
The inventories for agricultural machinery manufacture are specific to the different types of
276
machinery (tractors, harvesters, tillage implements or other implements).
277
The amount of machinery (AM) consumed for caring out a specific agricultural operation was
278
calculated by multiplying the weight (W) of the machinery by the operation time (OT) and
279
dividing the result by the lifetime of the machinery (LT) [44]:
280
AM (kg/FU) = W(kg)OT∙(h/FU)/LT(h)
281
where FU (see former Section 2.2) is the functional unit of the LCA. The operation time, the
282
lifetime and the weights of the machinery considered for all operations are obtained from the
283
Spanish platform for the knowledge of agricultural machinery [39].
284 285
2.4.4 Field and fertilizers derived emissions (Nitrous oxide emissions)
286
There is debate regarding methodologies [40,41,44] and factors that should be used for the
287
estimation of N2O emissions. This debate has been raised in several bioenegy life cycle
288
assessments and reviews [47-49] given that its importance is critical when assessing GWP
289
reductions [50]. To provide a good estimation of N2O emissions we follow the RSB GHG
290
Calculation Methodology v 2.1 [41] given that it reflects the present state of research and it is
291
based on methods which provide detail and consistency. Nitrate leaching emissions affecting to
292
N2O emissions have not been included because its estimation is based on the interrelation
293
several local parameters and is difficult to obtain meaningful estimates for Spain as a whole.
294
The calculation of the N2O emissions proposed by the RSB [41] is based on the formula in
295
Nemecek et Kägi [44] and adopts the IPCC guidelines [40]:
296
N2O(KgN2O/ha) = 44/28∙(EF1∙(Ntot+Ncr)+EF4∙14/17∙NH3)
297
where N2O(kg N2O/ha) are emissions of N2O; 44/28 is the conversion factor of N-N2O in N2O;
298
EF1(Kg N-N2O/kg N inputs) is the factor that gives the fraction of N inputs that is converted
299
into N2O (see Table 3); Ntot(kg N/ha) is the total nitrogen input from fertilizers; Ncr is the
300
nitrogen contained in the crop residues (kg N/ha);EF4 is the factor that gives the fraction of N-
301
NH3 that is converted in N-N2O (Kg N-N2O/kg N-NH3) (see Table 3); 14/17 is conversion
302
factor of NH3 in N-NH3; NH3 are the losses of nitrogen in the form of ammonia (kg NH3/ha)
303
calculated as proposed in the RSB [41] and Nemecek et Kägi [44] methodologies.
304 305 306
307
2.4.5 Parameter uncertainty and variability
308
Results obtained from LCA can have high uncertainties due to the considerable amounts of
309
measured and simulated data and the simplification of models that try to represent complex
310
environmental cause-effect chains [51]. There all several examples of quantitative uncertainty
311
assessments in LCA that deal with this problem [32] and more are appearing overt time.
312
However, the incorporation of uncertainty through the use of a stochastic modeling with Monte
313
Carlo Simulation is not yet a common practice for recent LCA studies, despite of the
314
recommendations that were made years ago [52].
315
We systematically included parameter uncertainty and variability for all the inventories that
316
have a significant importance for the results and then we performed Monte Carlo simulations.
317
When enough data was available a distribution was fitted to the parameter sample following a
318
scientific procedure. In order to fit the best possible distribution goodness of fit tests were
319
performed in combination with graphical tests as quantile-quantile plot. When the amount of
320
data was not enough to adjust a distribution following previous procedure, simpler distributions
321
(uniform or triangular) were chosen. We chose between triangular and uniform distributions and
322
adjusted them taking into account the values retrieved from the references we found and authors
323
knowledge of the phenomenon being studied (see Table 3). Percentiles of 5 % (P05) and 95 %
324
(P95) were calculated for each distribution in order to perform a sensitivity analysis to
325
determine the most influencing parameters (See Fig 5 and Fig 9). The ideal situation when
326
dealing with uncertainty and variability [31] is to separate their effects for each parameter and
327
perform a tiered Monte Carlo simulation [53]. However, is not possible separate the effects of
328
uncertainty and variability for all the parameters defined in this study, due to this fact and for
329
consistency reasons, the distributions shown in Table 3 consider the effects of uncertainty and
330
variability together.
331
332 333
Table 3 Uncertainty and variability of LCI parameters Parameter Units Distribution1 Wheat grain GY yield SG Straw/grain Straw storage SL losses Straw NHV NHVCP,0 0% Humidity Straw SH humidity Power plant PE efficiency Isohumic IHC coefficient Soil carbon CSOM factor Straw exportation SFC fertility compensation
334 335 336 337
P052
P953
Data Sources and Explanations
Last 15 years wheat yields available for Spain. 11 % Humidity 1633 3516 of grains was subtracted from data 0.91 1.09 Adapted to Spanish typical yields and cutting heights 2 % can fit well for Spanish dry weather. 0.011 0.029 A ± 50 % variation was considered appropriate.
kg/ha
LN (2462,582)
kg/kg kg lost/ kg stored
U (0.9, 1.1)
MJ/kg
LN (16.94, 0.99)
15.4
18.6 37 wheat straw samples from ECN Phyllis 2 database
% wb
T (12, 10, 16)
10.8
14.9 Based on officers from a Spanish 25 MW biomass power plant
T (0.30, 0.29, 0.31)
0.293 0.307 Based on officers from a Spanish 25 MW biomass power plant
T (0.10,0.08, 0.15)
0.088 0.137
MJe/ MJ straw kg SOM/ kg straw kg SOC/ kg SOM kg Fertilizer/ Nutrient Content
U (0.01, 0.03)
U (0.50, 0.56)
U (0.25, 0.75)
Refs [26] [54] [55] [37]
Appropriate for Spain when straw is buried without nitrogen [56-59] fertilizers application. Usually 0.58 value is used for this factor but this value is more 0.503 0.557 [60] a maximum than an average. 0.275 0.725 Based on Cherubini LCAs.
[14]
18, 18 and 20 samples were used from BIOBID database. LN (0.568, 0.180) 0.0326 0.901 kg Nutrient/ Negative values of distributions were converted to cero. T(0.188, -0.027, 0.221) 0.0247 0.201 kg Straw Straw K and P contents were transformed to K2O and P2O5 as T (1.63, -0.23, 1.63) 0.186 1.583 fertilizers doses are given this way.
[38]
IPCC Factors for N2O emissions. For both factors triangular 0.0061 0.0248 distributions with the typical values as modes and extreme 0.0064 0.0402 values of IPCC interval as maximum and minimum were used.
[40]
SN SK SP
Straw N Straw P2O5 Straw K2O
EF1 EF4
IPCC Factor 1 Kg N-N2O/ T (0.01, 0.003, 0.03) IPCC Factor 4 kg N inputs T (0.01, 0.002, 0.05)
Diesel & Oil Good estimate for consumption variation when conditions for DOCF Consumption U (0.75, 1.25) 0.775 1.225 and specific operation change. Factor Crop Land ha available/ Own estimation based on average biomass transport distance CLA T(0.03, 0.015, 0.045) 1.97 4.03 availability ha total provided by power plant owners. Natural Gas A ± 50 % variation from data provided by 25 MW power plant NGF U(0.5, 1.5) 0.55 1.45 Factor owners was considered appropriate. Ash & Slag A ± 25 % variation from data provided by 25 MW power plant ASPF U(0.75, 1.25) 0.775 1.225 production owners was considered appropriate. Ash & Slag A ± 25 % variation from data provided by 25 MW power plant ASTF U(0.75, 1.25) 0.775 1.225 transport owners was considered appropriate. Power plant A ± 25 % variation from data provided by 25 MW power plant PPEF U(0.75, 1.25) 0.775 1.225 emissions owners was considered appropriate. 1 LN: Log-Normal(average, standard deviation) U: Uniform(minimum, maximum) T: Triangular(mode, minimum, maximum). 2 Percentile of 5 % 3 Percentile of 95%
338
2.5. Life cycle impact assessment
339
In the Life Cycle Impact Assessment (LCIA) phase of an LCA the inputs and outputs of
340
elementary flows that have been collected and reported in the inventory are translated into
341
impact indicator results [61]. LCIA includes mandatory and optional steps. We carried out
342
mandatory steps of classification and characterization and avoided optional steps of
343
normalization and weighting optional steps.
344 345
2.5.1. Environmental impact assessment method
346
The impact assessment method chosen to evaluate the GWP is the IPCC 2007 100 years’ time
347
horizon [62]. The method calculates the cumulative radiative forcing caused by a unit mass
348
emission of a GHG, integrated over a 100 year time horizon, as compared with the cumulative
[39]
349
radiative forcing due to emission of a unit mass of carbon dioxide (CO2) over the same time
350
horizon.
351 352
2.5.2. Energy assessment method
353
Cumulative Energy Requirement Analysis (CERA) [63] was the method chosen to assess the
354
energy consumed to generate electricity from wheat straw biomass and from natural gas. This
355
method aims to calculate the energy use throughout the life cycle of a good or service.
356 357
3 Results
358 359
3.1 Global Warming Potential
360
In the Fig. 2 can be shown the probability distribution of the GWP generated in the production
361
of 1 TJe from wheat straw. The results were within the confidence interval that goes from 41 to
362
70 Mg COeq/TJe with a 5 % significance level. The mean value obtained was 55 Mg COeq/TJe,
363
this is a 62 % less than the 143 Mg COeq/TJe obtained when Spanish natural gas electricity was
364
simulated. When 1 TJe of natural gas electricity was compared with the same amount of
365
electricity coming from wheat straw, in 100 % of the simulations the GWP generated by natural
366
gas was higher.
367
368 369 370 371
Fig. 2. Probability distribution of GWP incurred in the generation of 1TJ of electricity from wheat straw in Spain. Monte Carlo simulation needed 811number of runs to obtain as standard error of the mean lower than 0.5 % of the average.
372
The European Commission recommends following the same sustainability criteria for solid and
373
gaseous biofuels [27] as the binding criteria established by the RED [64] for liquid biofuels.
374
Due to this fact, we have assumed that sustainable electricity production from wheat straw
375
should have at least 60% of GHG savings compared to the reference, as this is the amount
376
required by the RED for liquid biofuels after 2017. Fig. 3 shows in positive (green) the
377
exceeding GWP reduction of the simulations that accomplished the 60 % of GHG savings
378
criteria and in negative (red) the missing GWP to accomplish the same criteria. The results were
379
between -16 and 18 Mg COeq/TJe with a 5 % significance level. The generation of electricity
380
by straw burning under Spanish conditions accomplished the EU biomass sustainability criteria
381
for 58% of the 17205 simulations done. According to these results, production of electricity
382
from Spanish wheat straw was sustainable in the majority of cases but it should be borne in
383
mind that there is a probability of 38 % of being outside the EU sustainability zone.
384
385 386 387 388 389
Fig. 3. Probability distribution of the GWP of the simulations that saved more (in green) and less (in red) than the 60 % GHG saving threshold in the comparisons of wheat straw electricity and natural gas electricity under Spanish conditions. Monte Carlo simulation needed 17205 runs to obtaining a standard error of the mean lower than 0.5% of the average value of the simulations.
390
The GPW shares of the different phases of the production of straw electricity are shown in Fig.
391
4. The loss of soil organic carbon was the most contributing phase with 39.8 Mg CO2 eq./TJe
392
electricity followed by the compensation of fertility decrease with 9.0 Mg CO2 eq./TJe
393
electricity. The avoided N2O emissions due the lower amount of crop residues that remain into
394
the field had a remarkable negative contribution of 5.5 Mg CO2 eq./TJe. The phases of biomass
395
transport and the power plant operation had limited impact compared to others.
396
397 398 399
Fig. 4. GPW shares of Spanish wheat straw electricity for average values.
400
The sensitivity analysis of the most influencing parameters for the GWP and the phases of the
401
analysis that they are affecting can be shown in Fig. 5. It can be observed that for parameters
402
which are modelled with non- symmetric distributions (see Table 4) the upper (P95) and the
403
lower (P05) percentiles influences had remarkable differences between them. This is the case of
404
isohumic coefficient, net heating value, grain yield and IPCC factor 1. Isohumic coefficient was
405
the most sensitive parameter especially for the 95% percentile. The net heating value and the
406
fertility compensation factor also had big influences in the sensitivity analysis with a P05-P95
407
range that is more than 10 % the GWP of the average case.
408 409 410 411
Fig. 5. Sensitivity analysis of parameters which P05-P95 percentile ranges are more than 5 % of the average straw electricity GWP. The phases which parameters contribute to are shown in colored squares.
412
3.2 Cumulative Energy
413
The Fig. 6 shows in red the percentage of the simulations in which electricity from wheat straw
414
consumed more energy than electricity from natural gas for each type of energy source, the
415
opposite situation is presented in green. Total primary energy was higher for straw electricity in
416
more than 95 % of the simulations because the biomass energy content of the straw has been
417
included in the calculations. Nevertheless, non renewable fossil energy consumption was always
418
higher for natural gas electricity, being also the most consumed of the three non-renewable
419
sources of energy evaluated.
420 421 422 423
Fig. 6. Uncertainty analysis of the different sources of primary energy consumed in the generation of 1 TJ of electricity natural gas electricity minus 1 TJ of electricity from wheat straw.
424
The probability distribution of the fossil energy consumed to produce 1 TJ of electricity from
425
wheat straw is show in Fig. 7. The fossil energy consumption was studied separately due to the
426
non renewable and scarceness characteristics of fossil fuels. The fossil energy consumption for
427
straw electricity was within the confident interval that goes from 0.13 to 0.26 TJ fossil/TJ
428
electricity with a 5 % significance level. The mean value obtained was 0.18 TJ fossil/TJ
429
electricity, this is more than ten times less than the 2.44 TJ fossil/TJ electricity obtained for
430
natural gas electricity. The mean value of fossil energy consumption obtained for straw
431
electricity implies that the production of electrical energy was more than five times higher than
432
the fossil energy consumed for its generation.
433
434 435 436 437
Fig. 7. Probability distribution of fossil energy consumed in the generation of 1TJ of electricity from wheat straw in Spain. Monte Carlo simulation needed 1304 number of runs to obtain as standard error of the mean lower than 0.005.
438
The Fig. 8 shows the fossil energy shares of the production of straw electricity. The most fossil
439
energy consuming phase was the extra field works needed to export straw bales with 49 % of
440
the total. The fertilizers intensification to compensate fertility loss was the second phase in order
441
importance with 27% of the total. Bales transport and biomass plant operation accounted
442
together for 24 % of total fossil energy consumed.
443
444 445 446
Fig. 8. Fossil energy shares of Spanish wheat straw electricity for average values.
447
The sensitivity analysis of the most influencing parameters for the fossil energy consumption
448
and the phases that they are affecting can be shown in Fig. 9. The fertility compensation factor
449
was one of the most sensitive parameters for fossil energy, raising or lowering its consumption
450
in more than 12 % for both the 95 % and the 5 % percentile. The wheat grain yield was the
451
second parameter in order of influence, when it took the value of the 5 % percentile fossil
452
energy consumption raised more than 18 %. Diesel consumption factor and net heating value
453
had similar importance in this sensitivity analysis producing variations of around 10 % for 5%
454
and 95 % percentiles values with respect to average figures.
455
456 457 458 459
Fig. 9. Sensitivity analysis of parameters which P05-P95 percentile ranges are more than 5 % of the average straw electricity fossil energy demand. The phases which parameters contribute to are shown in colored squares.
460
4. Discussion
461 462
The average results presented in this article for the GWP assessment of wheat straw electricity
463
(55 Mg CO2 eq/TJe ) were very similar to previously published results for winter cereals grown
464
as dedicated energy crop for electricity generation in the Spanish province of Soria (52 Mg CO2
465
eq/TJe) [4] and slightly higher than the ones obtained for Brassica Carinata (47 Mg CO2
466
eq/TJe) cultivated also in Spain [65]. Upon comparing these values it should be borne in mind
467
that the methodologies used and the burdens of these studies have some differences between
468
them. The accounting of the loss of soil organic carbon due to wheat straw exportation is the
469
main reason for obtaining a higher GWP value for the LCA of a crop residue than for the LCAs
470
of previously mentioned dedicated energy crops.
471
Loss of soil organic carbon is the main driver of GWP in the production of electricity from
472
straw and its value is crucial to know if more than 60 % of GHG savings can be obtained when
473
comparing straw electricity with natural gas electricity in Spain. The isohumic coefficient and
474
the soil carbon factor are the parameters which influence the most this carbon loss. They depend
475
on local factors as crop residue and soil characteristics, rainfall, temperature, etc., and can be
476
estimated more precisely for a specific plot of terrain. Due to this fact, we recommend using
477
local parameters if available or develop local experiments to guess about them since the
478
accomplishment of EU sustainability criteria is highly dependent of these factors according to
479
our modelling. This is in line with the recommendations of recent studies [51,66] that
480
emphasized the importance of local aspects and case-specific conditions in LCAs results.
481
The selection wheat varieties more adapted to bioenergy with higher neat heating value and
482
lower straw nitrogen content can provide better results especially for fossil energy consumption.
483
In the selection of these varieties it should be borne in mind that the main objective of growing
484
wheat is grain production so varieties selection for bioenergy should respect grain quality and
485
productivity. Provided that the nitrogen content is a requirement for grains to achieve good
486
quality and high productivity, harvesting in the optimal moment when all nitrogen has migrated
487
to grains can be as well a good strategy that will help to minimize nitrogen content in straw.
488
Higher nitrogen content in straw is negative for fossil energy consumption because more
489
inorganic fertilizer will be used to compensate the loss of fertility due to straw exportation.
490
Nevertheless, higher nitrogen content in straw has opposing effects for GWP. The higher
491
nitrogen content in straw the more nitrous oxide is avoided due to lower emissions of crop
492
residues that are not left into the field any more. However, more inorganic nitrogen fertilisers
493
are needed to compensate fertility loss because of straw exportation and also more nitrous oxide
494
is emitted due to the application of these fertilizers. Doing further research to reduce the
495
uncertainty of the fertility compensation factor could help to provide more precise results.
496
Although many companies are not very willing to share information about their production
497
processes, LCA practitioners should try to obtaining country specific recent data on fertilizer
498
production systems [7]. This usually provides more precise inventories and results in lower
499
impacts due to the effect of more efficient production processes and new technologies that
500
capture GHGs. The probability of having high humidity in bales in Spain is not as high as in
501
other countries with higher precipitations in summer but using tarped cover storage could also
502
minimize storage losses and reduce straw humidity in case of rain [67].
503
Field works optimization is other important task to be performed to minimise GWP and energy
504
consumption. Proper sizing and maintenance of the tractor as well as performing field works at
505
the optimal velocity could help to reduce diesel consumption and therefore reduce GWP and
506
fossil energy consumption.
507
More efficient power plants will reduce impacts on GWP and fossil energy consumption.
508
However, a trade of exists since more efficient plants are usually bigger and they require more
509
biomass that is transported from further locations. A possible solution to minimise this raise of
510
the biomass transport distance could be to choose the best possible location for the plant and
511
stimulate more the local farmers stablished near the power plant to grow wheat sell their bales to
512
this industry.
513 514
4 Conclusions
515 516
The systematic inclusion of parameter uncertainty and variability for most influencing
517
parameters of the LCAs of bioenergy systems is an important task to be performed in the search
518
of more accurate results that help decision makers to give more supported answers to bioenergy
519
sustainability criteria accomplishment.
520
The production of electricity from wheat straw in Spain resulted in lower GHG emissions than
521
Spanish natural gas electricity for 100% of the simulations. However, only 58 % of the
522
simulation accomplished EU sustainability criteria of 60% GHG savings when compared with
523
natural gas electricity. This result implies that although production of electricity from straw is
524
sustainable for the majority of the cases, there is also a big probability of not accomplishing the
525
EU threshold, in which case most influencing phases should be analyzed searching ways to
526
minimize impacts.
527
The optimization of field works performed for straw exportation in combination with the
528
reduction of storage losses and bale humidity could slightly increase the GHG savings and
529
reduce the fossil energy consumption. The selection of wheat varieties with higher straw net
530
heating value could also be an improvement. However, as the sensitivity points out, the
531
parameters that affect the calculation of the soil organic carbon losses due to straw exportation
532
are the most influencing for GWP assessment and therefore they are crucial to clarify if the EU
533
sustainability criteria of 60 % GHG savings is accomplished for Spanish straw electricity. Due
534
to this fact, we understand that local and case specific estimation of soil carbon factor and
535
especially isohumic coefficient are unavoidable tasks to reduce the uncertainty and provide a
536
definitive answer to the accomplishment of EU sustainability criteria of a crop residue harvested
537
in a specific location.
538 539
Acknowledgements
540
The authors thank to Dr. Francisco Nieto for sharing his agricultural knowledge with us. We are
541
also grateful to Dr. Edgar Hertwich at the Industrial Ecology department of the Norwegian
542
University of Science and Technology for contributing with helpful comments on the drafts of
543
this manuscript.
544 545 546
547 548
References
549 550
[1] European Commission. Europe 2020: A Strategy for Smart, Sustainable and Inclusive Growth: Communication from the Commission. COM(2010) 2020 2010;2020.
551 552
[2] EEA. EU bioenergy potential from a resource-efficiency perspective. EEA (Environmental European Agency). Report No 6/2013. 201310.2800/92247.
553 554 555
[3] Gasol CM, Gabarrell X, Anton A, Rigola M, Carrasco J, Ciria P et al. LCA of poplar bioenergy system compared with Brassica carinata energy crop and natural gas in regional scenario. Biomass Bioenerg 2009;33:119-29. http://dx.doi.org/10.1016/j.biombioe.2008.04.020.
556 557 558 559
[4] Sastre CM, Maletta E, González-Arechavala Y, Ciria P, Santos AM, del Val A et al. Centralised electricity production from winter cereals biomass grown under central-northern Spain conditions: Global warming and energy yield assessments. Appl Energy 2014;114:737-48. http://dx.doi.org/10.1016/j.apenergy.2013.08.035.
560 561 562
[5] Lechón Y, Cabal H, de la Rúa C, Caldás N, Santamaría M, Sáez R. Energy and greenhouse gas emission savings of biofuels in Spain's transport fuel. The adoption of the EU policy on biofuels. Biomass Bioenerg 2009;33:920-32. http://dx.doi.org/10.1016/j.biombioe.2009.02.001.
563 564 565 566
[6] Hennecke AM, Faist M, Reinhardt J, Junquera V, Neeft J, Fehrenbach H. Biofuel greenhouse gas calculations under the European Renewable Energy Directive – A comparison of the BioGrace tool vs. the tool of the Roundtable on Sustainable Biofuels. Appl Energy 2013;102:55-62. http://dx.doi.org/10.1016/j.apenergy.2012.04.020.
567 568 569
[7] Ahlgren S, Röös E, Di Lucia L, Sundberg C, Hansson P. EU sustainability criteria for biofuels: uncertainties in GHG emissions from cultivation. Biofuels 2012;3:399-411. http://dx.doi.org/10.1016/10.4155/bfs.12.33.
570 571
[8] Kendall A, Yuan J. Comparing life cycle assessments of different biofuel options. Curr Opin ChemBiol 2013;17:439-43. http://dx.doi.org/10.1016/j.cbpa.2013.02.020.
572 573
[9] Muench S, Guenther E. A systematic review of bioenergy life cycle assessments. Appl Energy 2013;112:257-73. http://dx.doi.org/10.1016/j.apenergy.2013.06.001.
574 575 576
[10] Elsayed MA, Matthews R, Mortinmed ND. Carbon and Energy Balances for a range of biofuels options. Project final report. Project number B/B6/00784/REP. Resource Research Unit, Sheffield Hallam University and Forest Research. 2003.
577 578 579
[11] Dworak T, Elbersen B, van Diepen K, Staritsky I, van Kraalingen D, Suppit I et al. Assessment of inter-linkages between bioenergy development and water availability. Ecologic, Institute for International and European Environmental Policy Berlin/Vienna 2009;ENV.D.2/SER/2008/0003r:1-139.
580 581 582
[12] Cherubini F, Bird ND, Cowie A, Jungmeier G, Schlamadinger B, Woess-Gallasch S. Energy- and greenhouse gas-based LCA of biofuel and bioenergy systems: Key issues, ranges and recommendations. Resour Conserv Recycling 2009;53:434-47. http://dx.doi.org/10.1016/j.resconrec.2009.03.013.
583 584 585
[13] Kalt G, Kranzl L. Assessing the economic efficiency of bioenergy technologies in climate mitigation and fossil fuel replacement in Austria using a techno-economic approach. Appl Energy 2011;88:3665-84. http://dx.doi.org/10.1016/j.apenergy.2011.03.014.
586 587
[14] Cherubini F, Ulgiati S. Crop residues as raw materials for biorefinery systems – A LCA case study. Appl Energy 2010;87:47-57. http://dx.doi.org/10.1016/j.apenergy.2009.08.024.
588 589 590
[15] Parajuli R, Løkke S, Østergaard PA, Knudsen MT, Schmidt JH, Dalgaard T. Life Cycle Assessment of district heat production in a straw fired CHP plant. Biomass Bioenergy 2014;68:115-34. http://dx.doi.org/10.1016/j.biombioe.2014.06.005.
591 592 593
[16] Weiser C, Zeller V, Reinicke F, Wagner B, Majer S, Vetter A et al. Integrated assessment of sustainable cereal straw potential and different straw-based energy applications in Germany. Appl Energy 2014;114:749-62. http://dx.doi.org/10.1016/j.apenergy.2013.07.016.
594 595 596
[17] Giuntoli J, Boulamanti AK, Corrado S, Motegh M, Agostini A, Baxter D. Environmental impacts of future bioenergy pathways: the case of electricity from wheat straw bales and pellets. GCB Bioenergy 2013;5:497-512. http://dx.doi.org/10.1111/gcbb.12012.
597 598
[18] Dassanayake GDM, Kumar A. Techno-economic assessment of triticale straw for power generation. Appl Energy 2012;98:236-45. http://dx.doi.org/10.1016/j.apenergy.2012.03.030.
599 600 601
[19] Delivand MK, Barz M, Gheewala SH, Sajjakulnukit B. Economic feasibility assessment of rice straw utilization for electricity generating through combustion in Thailand. Appl Energy 2011;88:3651-8. http://dx.doi.org/10.1016/j.apenergy.2011.04.001.
602 603
[20] Zhang Q, Zhou D, Zhou P, Ding H. Cost Analysis of straw-based power generation in Jiangsu Province, China. Appl Energy 2013;102:785-93. http://dx.doi.org/10.1016/j.apenergy.2012.08.032.
604 605 606
[21] Yang J, Wang X, Ma H, Bai J, Jiang Y, Yu H. Potential usage, vertical value chain and challenge of biomass resource: Evidence from China's crop residues. Appl Energy 2014;114:717-23. http://dx.doi.org/10.1016/j.apenergy.2013.10.019.
607 608 609
[22] Esen H, Inalli M, Esen M. Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern Turkey. Energ Convers Manage 2006;47:1281-97. http://dx.doi.org/10.1016/j.enconman.2005.06.024.
610 611 612
[23] Esen H, Inalli M, Esen M. A techno-economic comparison of ground-coupled and air-coupled heat pump system for space cooling. Build Environ 2007;42:1955-65. http://dx.doi.org/10.1016/j.buildenv.2006.04.007.
613 614
[24] Esen M, Yuksel T. Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy Build 2013;65:340-51. http://dx.doi.org/10.1016/j.enbuild.2013.06.018.
615 616 617
[25] Nguyen TLT, Hermansen JE, Mogensen L. Environmental performance of crop residues as an energy source for electricity production: The case of wheat straw in Denmark. Appl Energy 2013;104:633-41. http://dx.doi.org/10.1016/j.apenergy.2012.11.057.
618 619
[26] FAOSTAT. Agriculture Organization of the United Nations. Faostat Database. Available at: http://faostat.fao.org/. Statistical Database 2013.
620 621
[27] European Comision. Report on sustainability requirements for the use of solid and gaseous biomass sources in electricity, heating and cooling. COM(2010)11 2010;11.
622 623 624
[28] Gregg JS, Izaurralde RC. Effect of crop residue harvest on long-term crop yield, soil erosion and nutrient balance: trade-offs for a sustainable bioenergy feedstock. Biofuels 2010;1:69-83. http://dx.doi.org/10.4155/bfs.09.8.
625 626 627
[29] Payraudeau S, van der Werf HMG, Vertès F. Analysis of the uncertainty associated with the estimation of nitrogen losses from farming systems. Agr Syst 2007;94:416-30. http://dx.doi.org/10.1016/j.agsy.2006.11.014.
628 629 630
[30] Reijnders L, Huijbregts M. Nitrous oxide emissions from liquid biofuel production in life cycle assessment. Current Opinion in Environmental Sustainability 2011;3:432-7. http://dx.doi.org/10.1016/j.cosust.2011.08.005.
631 632
[31] Huijbregts MAJ. Application of uncertainty and variability in LCA. Int J LCA 1998;3:273-80. http://dx.doi.org/10.1007/BF02979835.
633 634 635
[32] Lloyd SM, Ries R. Characterizing, Propagating, and Analyzing Uncertainty in Life-Cycle Assessment: A Survey of Quantitative Approaches. J Ind Ecol 2007;11:161-79. http://dx.doi.org/10.1162/jiec.2007.1136.
636 637 638
[33] van Zelm R, Huijbregts MAJ. Quantifying the Trade-off between Parameter and Model Structure Uncertainty in Life Cycle Impact Assessment. Environ Sci Technol 2013;47:9274-80. http://dx.doi.org/10.1021/es305107s.
639 640 641
[34] Mullins KA, Griffin WM, Matthews HS. Policy Implications of Uncertainty in Modeled Life-Cycle Greenhouse Gas Emissions of Biofuels. Environ Sci Technol 2011;45:132-8. http://dx.doi.org/10.1021/es1024993.
642 643
[35] ISO. 14040:2006. Environmental management-Life cycle assessment-Principles and framework. European Committee for Standardization. 2006.
644 645
[36] ISO. 14044:2006. Environmental Management Life Cycle Assessment “Requirements and Guidelines. European Committee for Standardization. 2006.
646 647
[37] Phyllis2. Energy research Centre of the Netherlands (ECN). Phyllis2 Database. Available at https://www.ecn.nl/phyllis2/. Statistical Database 2013.
648 649
[38] BIOBID. Vienna University of technology. BIOBID Database. Available at:Â http://www.vt.tuwien.ac.at/biobib/EN/. Statistical Database 2013.
650 651 652 653 654
[39] MAGRAMA. Observatory of technologies: Agricultural Machinery (In Spanish). Spanish Ministry of Agriculture, Food and Environment. Available at: http://www.magrama.gob.es/es/ministerio/servicios/informacion/plataforma-de-conocimiento-para-elmedio-rural-y-pesquero/observatorio-de-tecnologias-probadas/maquinaria-agricola/costes-aperosmaquinas.aspx. 2014.
655 656 657 658
[40] De Kleine C, Novoa RSA, Ogle S, Keith AS, Rochette P, Wirth TC. Chapter 11:N2O Emissions from Managed soils, and CO2 emissions from Lime and Urea Application. In: International Panel on Climate Change, editor. 2006 IPCC guidelines for national greenhouse gas inventories Volume 4: Agriculture, Forestry and Other Land Use: Intergovernmental Panel on Climate Change; 2006.
659 660
[41] Roundtable on Sustainable Biofuels. RSB GHG Calculation Methodology. 2012;RSB-STD-01-00301 (Version 2.1):1-109.
661 662 663
[42] Emmenegger M. F., Heck T, Jungbluth N. Erdgas. In: Sachbilanzen von Energiesystemen: Grundlagen für den ökologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in ökobilanzen für die Schweiz (ed. Dones R.). Final report ecoinvent data v2.0, No. 6-V. 2007.
664 665 666
[43] Frischknecht R, Jungbluth N, Althaus H, Doka G, Dones R, Heck T et al. The ecoinvent Database: Overview and Methodological Framework (7 pp). Int J LCA 2005;10:3-9. http://dx.doi.org/10.1065/lca2004.10.181.1.
667 668
[44] Nemecek T, Kägi T. Life cycle inventories of agricultural production systems. Ecoinvent v2.0 Final report No. 15 2007.
669 670 671
[45] Jungbluth N. Erdöl. In: Sachbilanzen von Energiesystemen: Grundlagen für den Ökologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in ökobilanzen für die Schweiz (ed. Dones R.). Final report ecoinvent data v2.0, No. 6-IV. 2007.
672 673
[46] SAEFL. Handbuch Offroad-Datenbank. Swiss Agency for the Environment, Forests and Landscape (SAEFL) 2000.
674 675
[47] Börjesson P. Good or bad bioethanol from a greenhouse gas perspective – What determines this? Appl Energy 2009;86:589-94. http://dx.doi.org/10.1016/j.apenergy.2008.11.025.
676 677 678
[48] Cherubini F, Bird ND, Cowie A, Jungmeier G, Schlamadinger B, Woess-Gallasch S. Energy- and greenhouse gas-based LCA of biofuel and bioenergy systems: Key issues, ranges and recommendations. Resour Conserv Recy 2009;53:434-47. http://dx.doi.org/10.1016/j.resconrec.2009.03.013.
679 680 681
[49] Malça J, Freire F. Life-cycle studies of biodiesel in Europe: A review addressing the variability of results and modeling issues. Renew Sust Energ Rev 2011;15:338-51. http://dx.doi.org/10.1016/j.rser.2010.09.013.
682 683 684
[50] Crutzen P, Mosier A, Smith K, Winiwarter W. N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atmos Chem Phys 2007;7:11191-205. http://dx.doi.org/10.5194/acpd-7-11191-2007.
685 686
[51] Hellweg S, Milà i Canals L. Emerging approaches, challenges and opportunities in life cycle assessment. Science 2014;344:1109-13. http://dx.doi.org/10.1126/science.1248361.
687 688 689
[52] Huijbregts MJ, Norris G, Bretz R, Ciroth A, Maurice B, von Bahr B et al. Framework for modelling data uncertainty in life cycle inventories. Int J LCA 2001;6:127-32. http://dx.doi.org/10.1007/BF02978728.
690 691 692 693
[53] M Hauck, Z J N Steinmann, I J Laurenzi, R Karuppiah , M.A.J.Huijbregts. How to quantify uncertainty and variability in life cycle assessment: the case of greenhouse gas emissions of gas power generation in the US. Environ Res Lett 2014;9:074005,(7pp). http://dx.doi.org/10.1088/17489326/9/7/074005.
694 695
[54] Dallemand J. Cereals Straw Resources for bioenergy in the European Union. 1st ed. Luxembourg: European Communities, 2007.
696 697 698
[55] Forsberg G. Biomass energy transport: Analysis of bioenergy transport chains using life cycle inventory method. Biomass Bioenergy 2000;19:17-30. http://dx.doi.org/10.1016/S0961-9534(00)000209.
699 700
[56] Hénin S, Gras R, Monnier G. Le profil cultural: l'état physique du sol et ses conséquences agronomiques. : Masson Paris., 1969.
701 702
[57] Gros A, Vivancos AD. Abonos: guía práctica de la fertilización. 7th ed. Madrid: Mundi Prensa, 1981.
703 704
[58] Boiffin J, Zagbahi JK, Sebillotte M. Systèmes de culture et statut organique des sols dans le Noyonnais: application du modèle de Hénin-Dupuis. Agronomie 1986;6:437-46.
705 706
[59] Urbano Terrón P. Fitotecnia: ingeniería de la producción vegetal. 1st ed. Madrid: Mundi Prensa, 2002.
707
[60] Howard P. The carbon-organic matter factor in various soil types. Oikos 1965;15:229-36.
708 709
[61] EC-JRC-IES. International Reference Life Cycle Data System (ILCD) Handbook - General guide for Life Cycle Assessment - Provisions and Action Steps. Fisrt edition. 2010.
710 711
[62] Althaus H.J., EMPA. IPCC 2007 method in: Implementation of Life Cycle Impact Assessment Methods. Final report ecoinvent v2.2 No. 3. 2010.
712 713 714
[63] Jungbluth N, Esu-services, Frischknecht R, Ecoinvent-Centre, EMPA. Cumulative energy demand method in: Implementation of Life Cycle Impact Assessment Methods. Final report ecoinvent v2.2 No. 3. 2010.
715 716 717
[64] European Parliament. Directive 2009/28/EC of European Parliament of the council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. DIRECTIVE 2009/30/EC 2009;30.
718 719 720
[65] Gasol CM, Gabarrell X, Anton A, Rigola M, Carrasco J, Ciria P et al. Life cycle assessment of a Brassica carinata bioenergy cropping system in southern Europe. Biomass Bioenergy 2007;31:543-55. 10.1016/j.biombioe.2007.01.026.
721 722 723
[66] Finnveden G, Hauschild MZ, Ekvall T, Guinée J, Heijungs R, Hellweg S et al. Recent developments in Life Cycle Assessment. J Environ Manage 2009;91:1-21. http://dx.doi.org/10.1016/j.jenvman.2009.06.018.
724 725
[67] Darr MJ, Shah A. Biomass storage: an update on industrial solutions for baled biomass feedstocks. Biofuels 2012;3:321-32. http://dx.doi.org/10.4155/bfs.12.23.
726
